Kiva Project Findings Final
Interim | Final |
---|
Contents
Area of study
The bulk of its loans, in terms of both amount and quantity, are funded in the Philippines, thus being the country of focus in our analysis. The Republic of Philippines is made up of 7107 tropical islands, having a total square area of 300,000km2 and being 65% mountainous (Net Industries, 2018). Although the country’s official languages are Filipino (Tagalog) and English, it is a country that has diverse regional cultures, with three languages serving as regional lingua francas: Ilokano in Northern and Central Luzon, Tagalog in Central and Southern Luzon, and Cebuano in the Visayas and Mindanao.
Philippines are divided into three major island groups: Luzon, Visayas, and Mindanao.
Luzon, the most populous and largest island in the Philippines, home to the country’s capital and major metropolis Manila. It leads the country in agriculture and industrial manufacturing, and more than half of the Filipino population lives on Luzon (Britannica). Luzon also consists of Palawan Island, a large island southwest of Manila.
Visayas is an island group located in the centre of Philippines. It consists of seven large islands and several hundred smaller ones, and the region is famous for agriculture and fishing (Britannica).
Mindanao is the second largest main island after Luzon. The island has narrow coastal plains, with broad, fertile basins and extensive swamps(Britannica). Mindanao has the strongest Muslim presence in Philippines amongst the three islands, whose dominant religion is Roman Catholic, and is home to most of the ethnic minorities. Agriculture is a key industry like other islands, while its textile and timber industries are also important due to deposits of raw materials.
With vast ethnic, cultural, economic and religious differences between various provinces, geospatial analysis is used to identify how do Kiva’s loan attributes differ across the Philippines.
Analysis
Kernel Density Analysis
Methodology
Kernel density function is a non-parametric method of estimating the probability density function (PDF) of a continuous random variable, and is non-parametric as the underlying distribution for the variable is not assumed. Each sample point will have its own weight function which represents its influence of the density values in the surrounding neighbourhood, and each ‘bump” is centred at the datum and spreads out symmetrically to cover the neighbouring values. The size of the “bump” represents the probability assigned at the neighbourhood of values around that datum, and the estimated model is the summation of all the kernel function “bumps”.
The Gaussian Kernel function, represent by k(u), follows a normal distribution curve to represent the intensity of different points. The density plot for the Gaussian function for Philippines is plotted below.
Figure 1: Screenshot of loan_themes_by_region.csv
Findings
Spatial Autocorrelation Analysis
Methodology
Findings
Reference
1. Net Industries. (n.d.). Philippines - History Background. Retrieved April 01, 2018, from http://education.stateuniversity.com/pages/1197/Philippines-HISTORY-BACKGROUND.html http://histclo.com/country/oce/phl/phl-reg.html
2. Celebioglu, F. and Dall'erba, S (2010) "Spatial disparities across the regions of Turkey: an exploratory spatial data analysis", Annals of Regional Science, Vol. 45, No. 2, p. 379-400
3. Yu, D and Wei (2008) "Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment", Papers in Regional Science, Vol 87, No. 1, pp 97-117
4. Elias, M and Rey, S.J. (2011) "Educational Performance and Spatial Convegence in Peru
5. Andy Mitchell,(2005), The ESRI Guide to GIS Analysis: Volume 2: Spatial Measurements & Statistics,”, p. 136-145 http://region-developpement.univ-tln.fr/fr/pdf/R33/Elias.pdf
6. Luc Amelin,(n.d.). Local Indicators of Spatial Association-LISA, Retrieved from https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00338.x
7. Smith, T. (n.d.). Spatial Weight Matrices. Retrieved April 08, 2018, from: https://www.seas.upenn.edu/~ese502/lab-content/extra_materials/SPATIAL%20WEIGHT%20MATRICES.pdf
8. Britannica. (2016, October 03). Mindanao. Retrieved April 08, 2018, from https://www.britannica.com/place/Mindanao
9. Britannica. (2016, October 03). Visayan Islands. Retrieved April 08, 2018, from https://www.britannica.com/place/Visayan-Islands
10. Roxas, N.R. & Fillone, A.M. Transportation (2016) 43: 661. https://doi-org.libproxy.smu.edu.sg/10.1007/s11116-015-9611-4
11. Philippine Statistics Authority: CountrySTAT Philippines, 2018. Retrieved on 15 April 2018 from http://countrystat.psa.gov.ph/